Dr Hanen Samouda
Dr Hanen Samouda is interested in the investigation of the physical and biological human characteristics and determinants, their interactions with people’s environment, their evolution overtime and how this may impact their health, with particular focus on vulnerable population subgroups (e.g., children), as well as on the role of body composition in cardio-metabolic health.
Her research activities have been focused on:
- Study of the determinants and implications of obesity and the metabolically healthy/unhealthy weight statuses;
- Investigation of the peripheral and ectopic fat as determinants of cardiometabolic health;
- Investigation of the protective role of the leg fat mass against a wide range of metabolic and cardiovascular risk factors, in young people;
- Development of innovative predictive models of several body compartments recognized as emergent cardiometabolic risk factors, such as visceral adiposity;
- Evaluation of the efficacy of overweight/obesity therapies;
- Assessment of the norms and classification systems in paediatric obesity.
These areas of expertise are extremely important in terms of public health and clinical epidemiology as they aim to improve our understanding of the mechanisms behind the development of cardiometabolic illnesses and to develop innovative diagnosis, treatment and prevention methods in order to promote healthy living from childhood throughout the lifespan.
- OSPEL: Overweight and obeSity in Paediatric population in Luxembourg (PI).
- In collaboration with: Centre Hospitalier de Luxembourg, ZithaKlinik, Luxembourg and the Faculty for Health engineering and management, University of Lille, Laboratory of Public Health, France
- Funding: The Ministry of Higher Education and Research (MESR); The Ministry of Health; National research Fund Luxembourg.
Observation of Cardiovascular Risk Factors in Luxembourg.
Funding: The MESR.
Commentary: Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review.
Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study.
- Deep Digital Phenotyping Research Unit
- Platform Bioinformatics
- Public Health Expertise
- Public Health Research
- Physical Activity, Sport and Health
- PHR Custom Group 3